| Day | A1 | A2 | A3 |
|---|---|---|---|
| Day 1 | Tuesday | Thursday | Wednesday |
| Day 2 | Wednesday | Friday | Thursday |
| Day 3 | Thursday | Saturday | Friday |
| Day 4 | Friday | Sunday | Saturday |
| Day 5 | Saturday | Monday | Sunday |
| Day 6 | Sunday | Tuesday | Monday |
| Day 7 | Monday | Wednesday | Tuesday |
AD 6698 Generative AI for Business Analytics
Schedule
1 Course Schedule
Please adhere to the due dates posted on the course website. Late work is not accepted.
1.1 Course Schedule
| Lecture | Title | Description | Online | On-Campus |
|---|---|---|---|---|
| L0.1 | Dev Setup: GitHub, Jupyter, VS Code, Colab | Course tools setup and basic version control with GitHub, notebooks, and IDEs. | nan | nan |
| L0.2 | Introduction to Neural Networks | Understanding the basics of neural networks as a foundation for generative models. | nan | nan |
| L1.1 | Introduction to Course, Natural Language, and Generative AI Landscape | Overview of generative AI, its business relevance, and natural language as a data type. | nan | nan |
| L1.2 | Introduction to Natural Language Processing | Core NLP concepts: tokenization, POS tagging, parsing, and text pre-processing. | nan | nan |
| L2.1 | Prompt Engineering Fundamentals | Learn prompt design, zero-/few-shot techniques, and ICL with business use cases. | nan | nan |
| L2.2 | Tokenization, Embeddings, and Vector Semantics | Deep dive into tokenization methods, word embeddings, and semantic search foundations. | nan | nan |
| L3.1 | LLM Internals: Attention, Transformers, and Positional Encoding | Explore transformer architecture, attention mechanisms, and context windows. | nan | nan |
| L3.2 | Fine-Tuning Pipelines & Data Handling | Set up custom model pipelines and format datasets for supervised fine-tuning. | nan | nan |
| L4.1 | RAG Basics: Embeddings, Chunking, Indexing | Understanding retrieval-augmented generation concepts and corpus preparation. | nan | nan |
| L4.2 | RAG Query Pipelines with LangChain & LlamaIndex | Build Q&A systems using vector DBs, LangChain chains, and document loaders. | nan | nan |
| L5.1 | LoRA, QLoRA, PEFT Techniques | Learn parameter-efficient fine-tuning methods to adapt base models. | nan | nan |
| L5.2 | Embeddings & Similarity Search | Work with sentence embeddings for document clustering, search, and relevance. | nan | nan |
| L6.1 | Memory & Context Management | Strategies for working with limited context: caching, summaries, and memory buffers. | nan | nan |
| L6.2 | Multimodal Models and Use Cases | Explore vision-language models and multimodal business applications. | nan | nan |
| L7.1 | Model Evaluation & GPT-Eval | Evaluate generated outputs using automated tools, manual rubrics, and hallucination detection. | nan | nan |
| L7.2 | Responsible AI & Bias Detection | Assess bias, fairness, and ethics in generated outputs using toolkits and guidelines. | nan | nan |
| L8.1 | Intro to Agents: ReAct, Tool Use | Overview of agent-based systems and tool-augmented LLM workflows. | nan | nan |
| L8.2 | Multi-Agent Systems (AutoGen, CrewAI) | Design and orchestrate multi-agent collaborative systems for enterprise tasks. | nan | nan |